Classification based Support Vector Machine on Frequency Domain and Wavelet Features in Signal Processing

Frequency domain based features and Wavelet features are extracted from the audio signals of the motorcycles. Frequency domain based classificationdeals with the spectrogram of the audio signals that explains the extraction of features, classification and the performance analysis of the proposed system.The key drawback of these features are more time consuming and therefore the time and frequency features are addressed separately. This makes the system not much efficient and produces very less classification accuracy. Wavelet Transform (WT) is a very efficient signal processing tool that estimates the signal in the time and frequency domain simultaneously by the transforming time domain signal into time and frequency domain. Here wavelet domain based features are extracted from the audio signals and the features are classified using the same SVM classifier. The performance of the wavelet domain based featuresare obtained separately and the performances are compared with time domain and frequency domain based features performances.